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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Related Experiment Video

Updated: Jun 14, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.

Jimin Park1, Daniel E Cook2, Pi-Chuan Chang2

  • 1UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA.

Biorxiv : the Preprint Server for Biology
|September 4, 2024
PubMed
Summary
This summary is machine-generated.

DeepSomatic, a new deep learning tool, accurately detects somatic variants like SNVs and indels using both short-read and long-read sequencing data. It also provides a valuable dataset for cancer genomics research.

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Last Updated: Jun 14, 2025

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Somatic variant detection is crucial for cancer genomics.
  • Long-read sequencing offers advantages over short-read for repeat mapping and variant phasing.
  • Existing methods often struggle with specific variant types or data formats.

Purpose of the Study:

  • Introduce DeepSomatic, a deep learning method for somatic variant detection.
  • Evaluate DeepSomatic's performance across short-read and long-read sequencing technologies.
  • Address the need for public training and benchmarking data in somatic variant detection.

Main Methods:

  • Developed DeepSomatic, a deep learning model for Single Nucleotide Variants (SNVs) and insertions/deletions (indels).
  • Applied DeepSomatic to whole-genome and exome sequencing data from tumor-normal, tumor-only, and FFPE samples.
  • Generated and released a dataset of five matched tumor-normal cell line pairs sequenced using Illumina, PacBio HiFi, and Oxford Nanopore Technologies.

Main Results:

  • DeepSomatic demonstrates superior performance in detecting somatic SNVs and indels compared to existing callers.
  • The method shows consistent accuracy across different sequencing technologies (short-read and long-read).
  • Performance is particularly strong for indel detection.

Conclusions:

  • DeepSomatic is a robust and versatile tool for somatic variant detection.
  • The open-source dataset facilitates further research and development in cancer genomics.
  • DeepSomatic advances the analysis of both short-read and long-read sequencing data for cancer research.